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About This Role
Redwire is an innovative space and defense technology company driving advanced capabilities across the aerospace sector. We are looking for a leader who can contribute to concept of operations development, mission solution design, and strategic analysis for spacecraft programs supporting National Security Space. This includes work spanning space domain awareness, space resiliency, mission autonomy, structures, sensors, and next‑generation space systems.
The AI/ML Engineer designs, builds, and scales the backend, data, and computational infrastructure that powers intelligent agents, autonomous workflows, and AI‑driven decision systems. This role blends high‑performance backend engineering, graph‑centric data modeling, real‑time processing, and secure API design with emerging agentic architectures. You will work across simulation, data, and systems engineering teams to deliver intelligent, reliable, and mission‑aligned agentic capabilities supporting aerospace, defense, and other high‑integrity environments.
Major Responsibilities
- Develop agentic system capabilities — Build and integrate AI agents, autonomous workflows, and LLM‑driven decision systems into backend architectures.
- Design high‑performance backend services — Implement low‑latency, high‑throughput services in Python, C\+\+, or Rust.
- Architect real‑time processing pipelines — Build deterministic, concurrent, or multi‑threaded pipelines for real‑time agentic decision loops.
- Develop and govern data‑access layers — Implement indexing, query optimization, and data‑model governance for evolving knowledge domains.
- Build and optimize APIs — Design REST, GraphQL, and gRPC interfaces with strong schema governance and versioning.
- Integrate graph‑centric data systems — Model agent memory, context graphs, and reasoning structures using graph databases.
- Ensure reliability and observability — Implement logging, metrics, tracing, error handling, and automated testing.
- Collaborate across engineering domains — Work with systems engineers, simulation experts, analysts, and DevOps to define clean integration boundaries.
- Support secure and compliant operations — Apply authentication, authorization, secrets management, and secure‑by‑design principles.
Ideal Experience
- STEM foundation — Bachelor’s degree in CS, Engineering, Mathematics, or related field, or equivalent experience.
- Backend \& systems engineering — 1–5 years building backend systems, distributed services, or data‑driven pipelines.
- High‑performance programming — Proficiency in Python, C\+\+, or Rust for low‑latency or high‑throughput systems.
- Agentic system integration — Experience integrating AI agents, autonomous workflows, or LLM‑based decision systems.
- Graph‑centric data modeling — Experience with Neo4j, DGraph, ArangoDB, or similar technologies.
- Database schema \& modeling — Experience with relational, graph, and document databases.
- Real‑time processing — Experience with concurrent, deterministic, or multi‑threaded pipelines.
- High‑throughput data APIs — Experience with streaming systems and binary transport formats.
- Networking \& data transport — Expertise with UDP/TCP, Pub/Sub, and distributed messaging.
- GPU‑accelerated computation — Understanding of CUDA, GPU kernels, or heterogeneous compute architectures.
- API design expertise — Experience designing REST, GraphQL, and gRPC APIs.
- Microservice architectures — Familiarity with containerized deployments and service‑to‑service patterns.
- CI/CD integration — Experience integrating backend services into CI/CD pipelines.
- Service reliability fundamentals — Observability, error handling, contract validation, and automated testing.
- API \& data security — Strong understanding of authentication, authorization, and secure data‑access patterns.
- Engineering rigor — Experience working in aerospace/defense or other high‑integrity environments.
- Security eligibility — U.S. Citizen; able to obtain and maintain a DoD Secret clearance (TS/SCI preferred).
Desired Skills
- Multi‑protocol API development — REST, gRPC, SOAP, GraphQL.
- Agent‑oriented data structures — Modeling agent memory, context graphs, or reasoning chains.
- HPC‑adjacent workflows — Simulation data, scientific computation, or data‑dense analytics.
- Simulation \& modeling systems — Integrating AI agents with simulation engines or digital‑engineering tools.
- Distributed computation frameworks — Job orchestration, distributed compute, or Monte Carlo automation.
- High‑rate data processing — Optimizing ingestion and processing for high‑rate sensor or telemetry data.
- Regulated industry exposure — Aerospace, defense, robotics, or similar domains.
- Internal tooling development — Tools or libraries used across engineering teams.
- Cross‑functional collaboration — Work with systems engineers, analysts, simulation experts, and product teams.
- Open‑source contributions — Contributions to backend frameworks, agent libraries, or data‑modeling tools.
*Grow with us as we innovate the next generation capabilities for a new era of space exploration!* *We offer a highly competitive benefits package along with a commitment to our core values of Integrity, Innovation, Impact, Inclusion, and Excellence.*
*Don’t* *meet every single requirement above? No worries.* *We want people who can grow,* *collaborate* *and build a stronger team.* *We strive to build a diverse and inclusive culture, so if* *you**’**re* *excited about this job posting, we encourage you to apply.* *You may be just the right candidate for this or other roles.*
Redwire Space is an Equal Opportunity Employer; employment with MIS is governed on the basis of merit, competence and qualifications and will not be influenced in any manner by race, color, religion, gender, national origin/ethnicity, veteran status, disability status, age, sexual orientation, gender identity, marital status, mental or physical disability or any other legally protected status.
*All offers of employment at Redwire Space are contingent upon clear results of a thorough background check.*
To conform to U.S. Government space technology export regulations, including the International Traffic in Arms Regulations (ITAR) you must be a U.S. citizen, lawful permanent resident of the U.S., protected individual as defined by 8 U.S.C. 1324b(a)(3\), or eligible to obtain the required authorizations from the U.S. Department of State. Learn more about the Click Here
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Redwire Space, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Redwire Space AI Hiring
Redwire Space has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Chantilly, VA, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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